Microtexture Inpainting using Gaussian Models
Stochastic synthesis of micro-textures using ADSN model
Project Overview
This project focuses on micro-texture inpainting, where missing parts of images are reconstructed using stochastic modeling techniques. Micro-textures, which lack strong geometric patterns, can be effectively modeled as Gaussian random fields, enabling their synthesis through probabilistic methods.
Methodology
- ADSN Model: Asymptotic Discrete Spot Noise for generating micro-textures
- Kriging Conditioning: Ensures continuity at mask boundaries
- Color Adaptation: Extension to color images while preserving inter-channel correlations
- Covariance Matrix: Derived from auto-covariance function of micro-textures
Key Results
- Grayscale and color micro-texture synthesis
- Seamless inpainting with smooth boundary transitions
- MSE at contour in order of 10^-21, demonstrating excellent continuity
- Statistically consistent results
Technologies
- Language: Python (Jupyter Notebook)
- Libraries: NumPy, SciPy, Matplotlib
- Methods: Gaussian random fields, Kriging interpolation, ADSN model
Project Report
View or download the full project report:
Microtexture Inpainting Report